AIMC Topic: Computational Biology

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CustOmics: A versatile deep-learning based strategy for multi-omics integration.

PLoS computational biology
The availability of patient cohorts with several types of omics data opens new perspectives for exploring the disease's underlying biological processes and developing predictive models. It also comes with new challenges in computational biology in te...

DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning.

Molecules (Basel, Switzerland)
The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA's subcellular localization through wet-lab experiments is time-consuming and expensive,...

ProteInfer, deep neural networks for protein functional inference.

eLife
Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. Traditional approaches use sequence alignment to compare a query sequence either to thousands of models of protein families or to large ...

Real-to-bin conversion for protein residue distances.

Computational biology and chemistry
Protein Structure Prediction (PSP) has achieved significant progress lately. Prediction of inter-residue distances by machine learning and their exploitation during the conformational search is largely among the critical factors behind the progress. ...

Strategy toward Kinase-Selective Drug Discovery.

Journal of chemical theory and computation
Kinase drug selectivity is the ground challenge in cancer research. Due to the structurally similar kinase drug pockets, off-target inhibitor toxicity has been a major cause for clinical trial failures. The pockets are similar but not identical. Here...

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors.

Current opinion in structural biology
The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between...

RNA independent fragment partition method based on deep learning for RNA secondary structure prediction.

Scientific reports
The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures o...

MGREL: A multi-graph representation learning-based ensemble learning method for gene-disease association prediction.

Computers in biology and medicine
The identification of gene-disease associations plays an important role in the exploration of pathogenic mechanisms and therapeutic targets. Computational methods have been regarded as an effective way to discover the potential gene-disease associati...

Identification of Key MicroRNAs and Genes between Colorectal Adenoma and Colorectal Cancer via Deep Learning on GEO Databases and Bioinformatics.

Contrast media & molecular imaging
BACKGROUND: Deep learning techniques are gaining momentum in medical research. Colorectal adenoma (CRA) is a precancerous lesion that may develop into colorectal cancer (CRC) and its etiology and pathogenesis are unclear. This study aims to identify ...

A Review of Fusion Methods for Omics and Imaging Data.

IEEE/ACM transactions on computational biology and bioinformatics
The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e., omics-imaging fusion, offers a new strategy for understand...